scholarly journals Local Outcomes of Federal and State Urban & Community Forestry Programs

2011 ◽  
Vol 37 (4) ◽  
pp. 152-159
Author(s):  
Richard Hauer ◽  
Gary Johnson ◽  
Michael Kilgore
Keyword(s):  
United States ◽  
Regression Model ◽  
Multiple Regression ◽  
Community Forestry ◽  
Urban Community ◽  
The United States ◽  
Multiple Regression Model ◽  
Cost Share ◽  
Rate Of Increase ◽  
Forestry Programs

Increasing local urban and community forestry (U&CF) programs and activities in the United States is a goal of state and federal U&CF programs. This study found local U&CF programs within the 50 United States increased in activity between 1997 and 2002 at a 2.1% annual rate of increase. Several attributes of state U&CF forestry programs from a multiple regression model and correla-tion analysis partially explain the increase in local U&CF program activity. The number of technical assists in a state were a strong pre-dictor for increased local activity. Less certainty was found with state money used to fund the state U&CF program or the use of cost-share assistance (Federal Cooperative Forestry Assistance Challenge Cost-share Grants) and this increase. Study findings provide evidence that state and federal U&CF programs within the United States are furthering the building of capacity and development of local U&CF programs.

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

Reducing Rural Car Ownership: Cultural Not Policy Changes?

2018 ◽  
Vol 2672 (6) ◽  
pp. 1-10 ◽  
Author(s):  
Emma L. Rearick ◽  
Gregory L. Newmark
Keyword(s):  
United States ◽  
Land Use ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
The United States ◽  
Multiple Regression Model ◽  
Census Bureau ◽  
Matched Pair ◽  
Car Ownership ◽  
Quarter Century

Automobile use is recognized as affecting public health, environmental sustainability, land use, and household expense. Car use is closely tied to car ownership rates. Most car ownership research focuses on urban areas; however, 97% of the United States’ land area and a fifth of its population remains rural. Factors that affect car ownership in these communities may be different than in more urbanized areas. This research focuses on the 2,285 counties in the continental United States that are defined as entirely rural by the guidelines established in the Agricultural Act of 2014. These counties were grouped by five multi-state regions using U.S. Census Bureau definitions. Their percentage changes in car ownership, as well as other demographic variables, over a quarter century were calculated using data from the 1990 Decennial Census and the 2014 5-Year American Community Survey. A multiple regression model was estimated for each grouping to identify counties with lower-than-expected changes in car ownership. For each grouping, one of these outlying counties was selected and matched with another county whose changes in car ownership were within expected ranges given demographic developments. Local professionals were then interviewed to identify policies possibly responsible for the difference in car ownership trends between the matched-pair counties. The interviews suggested that, contrary to expectation, transportation policies had no discernable effect on rural car ownership, but land use polices and, more often, cultural factors linked to changing populations were associated with reduced rural car ownership.

scholarly journals COVID-19 Tweets of Governors and Health Experts

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tim Hua ◽  
Chris Chankyo Kim ◽  
Zihan Zhang ◽  
Alex Lyford
Keyword(s):  
United States ◽  
Regression Model ◽  
State Level ◽  
The United States ◽  
Similar Rate ◽  
Significant Finding ◽  
Party Affiliation ◽  
Substantial Evidence ◽  
Python Package ◽  
Health Experts

As COVID-19 spread throughout the United States, governors and health experts (HEs) received a surge in followers on Twitter. This paper seeks to investigate how HEs, Democratic governors, and Republican governors discuss COVID-19 on Twitter. Tweets dating from January 1st, 2020 to October 18th, 2020 from official accounts of all fifty governors and 46 prominent U.S.-based HEs were scraped using python package Twint (N = 192,403) and analyzed using a custom-built wordcount program (Twintproject, 2020). The most significant finding is that in 2020, Democratic governors mentioned death at 4.03 times the rate of Republican governors in their COVID-19 tweets. In 2019, Democratic governors still mentioned death at twice the rate of Republicans. We believe we have substantial evidence that Republican governors are less comfortable talking about death than their Democratic counterparts. We also found that Democratic governors tweet about masks, stay-at-home measures, and solutions more often than Republicans. After controlling for state-level variations in COVID-19 data, our regression model confirms that party affiliation is still correlated with the prevalence of tweets in these three categories. However, there isn’t a large difference between the proportion of COVID-19 tweets, tweets about the economy, tweets about vaccines, and tweets containing “science-like” words between governors of the two parties. HEs tweeted about death and vaccines more than the governors. They also tweeted about solutions and testing at a similar rate compared to governors and mentioned lockdowns, the economy, and masks less frequently.

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